Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations7842
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 MiB
Average record size in memory567.0 B

Variable types

Numeric8
Categorical6
Boolean2

Alerts

customer_id is highly overall correlated with housing and 1 other fieldsHigh correlation
education is highly overall correlated with jobHigh correlation
housing is highly overall correlated with customer_idHigh correlation
job is highly overall correlated with educationHigh correlation
month is highly overall correlated with customer_idHigh correlation
contact is highly imbalanced (61.7%) Imbalance
previous is highly skewed (γ1 = 27.99539649) Skewed
customer_id is uniformly distributed Uniform
customer_id has unique values Unique
balance has 432 (5.5%) zeros Zeros

Reproduction

Analysis started2024-12-21 20:36:18.882427
Analysis finished2024-12-21 20:36:40.900829
Duration22.02 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct7842
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3921.5
Minimum1
Maximum7842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2024-12-21T20:36:41.139009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile393.05
Q11961.25
median3921.5
Q35881.75
95-th percentile7449.95
Maximum7842
Range7841
Interquartile range (IQR)3920.5

Descriptive statistics

Standard deviation2263.9347
Coefficient of variation (CV)0.57731346
Kurtosis-1.2
Mean3921.5
Median Absolute Deviation (MAD)1960.5
Skewness0
Sum30752403
Variance5125400.5
MonotonicityStrictly increasing
2024-12-21T20:36:41.573906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5238 1
 
< 0.1%
5236 1
 
< 0.1%
5235 1
 
< 0.1%
5234 1
 
< 0.1%
5233 1
 
< 0.1%
5232 1
 
< 0.1%
5231 1
 
< 0.1%
5230 1
 
< 0.1%
5229 1
 
< 0.1%
Other values (7832) 7832
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
7842 1
< 0.1%
7841 1
< 0.1%
7840 1
< 0.1%
7839 1
< 0.1%
7838 1
< 0.1%
7837 1
< 0.1%
7836 1
< 0.1%
7835 1
< 0.1%
7834 1
< 0.1%
7833 1
< 0.1%

age
Real number (ℝ)

Distinct70
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.783856
Minimum18
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2024-12-21T20:36:41.923605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile27
Q132
median38
Q347
95-th percentile60.95
Maximum89
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.282964
Coefficient of variation (CV)0.2766527
Kurtosis0.8694297
Mean40.783856
Median Absolute Deviation (MAD)7
Skewness0.9747082
Sum319827
Variance127.30527
MonotonicityNot monotonic
2024-12-21T20:36:42.309292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 412
 
5.3%
32 393
 
5.0%
34 381
 
4.9%
37 368
 
4.7%
35 358
 
4.6%
31 349
 
4.5%
36 317
 
4.0%
30 301
 
3.8%
38 280
 
3.6%
39 249
 
3.2%
Other values (60) 4434
56.5%
ValueCountFrequency (%)
18 2
 
< 0.1%
19 9
 
0.1%
20 10
 
0.1%
21 16
 
0.2%
22 26
 
0.3%
23 43
 
0.5%
24 51
 
0.7%
25 64
0.8%
26 132
1.7%
27 158
2.0%
ValueCountFrequency (%)
89 1
 
< 0.1%
88 1
 
< 0.1%
86 3
 
< 0.1%
84 5
 
0.1%
83 8
0.1%
82 7
0.1%
81 3
 
< 0.1%
80 14
0.2%
79 11
0.1%
78 4
 
0.1%

job
Categorical

High correlation 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size508.3 KiB
management
1753 
blue-collar
1537 
technician
1289 
admin.
1057 
services
682 
Other values (6)
1524 

Length

Max length13
Median length12
Mean length9.3532262
Min length6

Characters and Unicode

Total characters73348
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadmin.
2nd rowadmin.
3rd rowservices
4th rowmanagement
5th rowmanagement

Common Values

ValueCountFrequency (%)
management 1753
22.4%
blue-collar 1537
19.6%
technician 1289
16.4%
admin. 1057
13.5%
services 682
 
8.7%
retired 458
 
5.8%
self-employed 264
 
3.4%
student 237
 
3.0%
entrepreneur 211
 
2.7%
unemployed 208
 
2.7%

Length

2024-12-21T20:36:42.678861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
management 1753
22.4%
blue-collar 1537
19.6%
technician 1289
16.4%
admin 1057
13.5%
services 682
 
8.7%
retired 458
 
5.8%
self-employed 264
 
3.4%
student 237
 
3.0%
entrepreneur 211
 
2.7%
unemployed 208
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e 11047
15.1%
n 8008
10.9%
a 7535
10.3%
l 5347
 
7.3%
m 5181
 
7.1%
i 4921
 
6.7%
c 4797
 
6.5%
t 4185
 
5.7%
r 3768
 
5.1%
d 2370
 
3.2%
Other values (12) 16189
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73348
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 11047
15.1%
n 8008
10.9%
a 7535
10.3%
l 5347
 
7.3%
m 5181
 
7.1%
i 4921
 
6.7%
c 4797
 
6.5%
t 4185
 
5.7%
r 3768
 
5.1%
d 2370
 
3.2%
Other values (12) 16189
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73348
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 11047
15.1%
n 8008
10.9%
a 7535
10.3%
l 5347
 
7.3%
m 5181
 
7.1%
i 4921
 
6.7%
c 4797
 
6.5%
t 4185
 
5.7%
r 3768
 
5.1%
d 2370
 
3.2%
Other values (12) 16189
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73348
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 11047
15.1%
n 8008
10.9%
a 7535
10.3%
l 5347
 
7.3%
m 5181
 
7.1%
i 4921
 
6.7%
c 4797
 
6.5%
t 4185
 
5.7%
r 3768
 
5.1%
d 2370
 
3.2%
Other values (12) 16189
22.1%

marital_status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size488.7 KiB
married
4501 
single
2454 
divorced
887 

Length

Max length8
Median length7
Mean length6.8001785
Min length6

Characters and Unicode

Total characters53327
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowsingle
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married 4501
57.4%
single 2454
31.3%
divorced 887
 
11.3%

Length

2024-12-21T20:36:43.008125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T20:36:43.307960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
married 4501
57.4%
single 2454
31.3%
divorced 887
 
11.3%

Most occurring characters

ValueCountFrequency (%)
r 9889
18.5%
i 7842
14.7%
e 7842
14.7%
d 6275
11.8%
m 4501
8.4%
a 4501
8.4%
s 2454
 
4.6%
n 2454
 
4.6%
g 2454
 
4.6%
l 2454
 
4.6%
Other values (3) 2661
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53327
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 9889
18.5%
i 7842
14.7%
e 7842
14.7%
d 6275
11.8%
m 4501
8.4%
a 4501
8.4%
s 2454
 
4.6%
n 2454
 
4.6%
g 2454
 
4.6%
l 2454
 
4.6%
Other values (3) 2661
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53327
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 9889
18.5%
i 7842
14.7%
e 7842
14.7%
d 6275
11.8%
m 4501
8.4%
a 4501
8.4%
s 2454
 
4.6%
n 2454
 
4.6%
g 2454
 
4.6%
l 2454
 
4.6%
Other values (3) 2661
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53327
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 9889
18.5%
i 7842
14.7%
e 7842
14.7%
d 6275
11.8%
m 4501
8.4%
a 4501
8.4%
s 2454
 
4.6%
n 2454
 
4.6%
g 2454
 
4.6%
l 2454
 
4.6%
Other values (3) 2661
 
5.0%

education
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size501.0 KiB
secondary
4197 
tertiary
2633 
primary
1012 

Length

Max length9
Median length9
Mean length8.4061464
Min length7

Characters and Unicode

Total characters65921
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtertiary
2nd rowsecondary
3rd rowsecondary
4th rowtertiary
5th rowtertiary

Common Values

ValueCountFrequency (%)
secondary 4197
53.5%
tertiary 2633
33.6%
primary 1012
 
12.9%

Length

2024-12-21T20:36:43.606055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T20:36:43.872593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
secondary 4197
53.5%
tertiary 2633
33.6%
primary 1012
 
12.9%

Most occurring characters

ValueCountFrequency (%)
r 11487
17.4%
a 7842
11.9%
y 7842
11.9%
e 6830
10.4%
t 5266
8.0%
s 4197
 
6.4%
c 4197
 
6.4%
o 4197
 
6.4%
n 4197
 
6.4%
d 4197
 
6.4%
Other values (3) 5669
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 11487
17.4%
a 7842
11.9%
y 7842
11.9%
e 6830
10.4%
t 5266
8.0%
s 4197
 
6.4%
c 4197
 
6.4%
o 4197
 
6.4%
n 4197
 
6.4%
d 4197
 
6.4%
Other values (3) 5669
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 11487
17.4%
a 7842
11.9%
y 7842
11.9%
e 6830
10.4%
t 5266
8.0%
s 4197
 
6.4%
c 4197
 
6.4%
o 4197
 
6.4%
n 4197
 
6.4%
d 4197
 
6.4%
Other values (3) 5669
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 11487
17.4%
a 7842
11.9%
y 7842
11.9%
e 6830
10.4%
t 5266
8.0%
s 4197
 
6.4%
c 4197
 
6.4%
o 4197
 
6.4%
n 4197
 
6.4%
d 4197
 
6.4%
Other values (3) 5669
8.6%

balance
Real number (ℝ)

Zeros 

Distinct3090
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1552.3433
Minimum-1884
Maximum81204
Zeros432
Zeros (%)5.5%
Negative471
Negative (%)6.0%
Memory size61.4 KiB
2024-12-21T20:36:44.169943image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-1884
5-th percentile-60.95
Q1162
median595
Q31733.75
95-th percentile6179.85
Maximum81204
Range83088
Interquartile range (IQR)1571.75

Descriptive statistics

Standard deviation3084.58
Coefficient of variation (CV)1.9870476
Kurtosis142.56241
Mean1552.3433
Median Absolute Deviation (MAD)559
Skewness8.2429717
Sum12173476
Variance9514633.8
MonotonicityNot monotonic
2024-12-21T20:36:44.521648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 432
 
5.5%
1 20
 
0.3%
2 20
 
0.3%
5 17
 
0.2%
4 17
 
0.2%
201 14
 
0.2%
3 14
 
0.2%
393 13
 
0.2%
315 12
 
0.2%
6 12
 
0.2%
Other values (3080) 7271
92.7%
ValueCountFrequency (%)
-1884 1
< 0.1%
-1882 1
< 0.1%
-1621 1
< 0.1%
-1400 1
< 0.1%
-1329 1
< 0.1%
-1212 1
< 0.1%
-1112 1
< 0.1%
-1083 1
< 0.1%
-982 1
< 0.1%
-980 1
< 0.1%
ValueCountFrequency (%)
81204 2
< 0.1%
52587 1
 
< 0.1%
37378 1
 
< 0.1%
34230 1
 
< 0.1%
32948 1
 
< 0.1%
31630 1
 
< 0.1%
29941 1
 
< 0.1%
29340 1
 
< 0.1%
29080 1
 
< 0.1%
27696 4
0.1%

housing
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
True
4942 
False
2900 
ValueCountFrequency (%)
True 4942
63.0%
False 2900
37.0%
2024-12-21T20:36:44.786076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

loan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
False
6753 
True
1089 
ValueCountFrequency (%)
False 6753
86.1%
True 1089
 
13.9%
2024-12-21T20:36:45.009550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

contact
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size498.5 KiB
cellular
7257 
telephone
 
585

Length

Max length9
Median length8
Mean length8.0745983
Min length8

Characters and Unicode

Total characters63321
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular 7257
92.5%
telephone 585
 
7.5%

Length

2024-12-21T20:36:45.279345image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T20:36:45.557914image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
cellular 7257
92.5%
telephone 585
 
7.5%

Most occurring characters

ValueCountFrequency (%)
l 22356
35.3%
e 9012
14.2%
c 7257
 
11.5%
u 7257
 
11.5%
a 7257
 
11.5%
r 7257
 
11.5%
t 585
 
0.9%
p 585
 
0.9%
h 585
 
0.9%
o 585
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 22356
35.3%
e 9012
14.2%
c 7257
 
11.5%
u 7257
 
11.5%
a 7257
 
11.5%
r 7257
 
11.5%
t 585
 
0.9%
p 585
 
0.9%
h 585
 
0.9%
o 585
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 22356
35.3%
e 9012
14.2%
c 7257
 
11.5%
u 7257
 
11.5%
a 7257
 
11.5%
r 7257
 
11.5%
t 585
 
0.9%
p 585
 
0.9%
h 585
 
0.9%
o 585
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 22356
35.3%
e 9012
14.2%
c 7257
 
11.5%
u 7257
 
11.5%
a 7257
 
11.5%
r 7257
 
11.5%
t 585
 
0.9%
p 585
 
0.9%
h 585
 
0.9%
o 585
 
0.9%

day_of_week
Real number (ℝ)

Distinct31
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.26001
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2024-12-21T20:36:45.801715image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median14
Q320
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.8853282
Coefficient of variation (CV)0.55296792
Kurtosis-0.75261059
Mean14.26001
Median Absolute Deviation (MAD)6
Skewness0.27183789
Sum111827
Variance62.178401
MonotonicityNot monotonic
2024-12-21T20:36:46.296580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
18 491
 
6.3%
15 453
 
5.8%
17 431
 
5.5%
20 431
 
5.5%
13 418
 
5.3%
12 380
 
4.8%
14 352
 
4.5%
5 349
 
4.5%
6 341
 
4.3%
4 336
 
4.3%
Other values (21) 3860
49.2%
ValueCountFrequency (%)
1 107
 
1.4%
2 307
3.9%
3 237
3.0%
4 336
4.3%
5 349
4.5%
6 341
4.3%
7 320
4.1%
8 294
3.7%
9 183
2.3%
10 99
 
1.3%
ValueCountFrequency (%)
31 30
 
0.4%
30 241
3.1%
29 270
3.4%
28 164
2.1%
27 93
 
1.2%
26 129
1.6%
25 111
1.4%
24 50
 
0.6%
23 67
 
0.9%
22 107
 
1.4%

month
Categorical

High correlation 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size459.6 KiB
may
2436 
nov
1093 
apr
1075 
feb
881 
aug
493 
Other values (7)
1864 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23526
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowoct
2nd rowoct
3rd rowoct
4th rowoct
5th rowoct

Common Values

ValueCountFrequency (%)
may 2436
31.1%
nov 1093
13.9%
apr 1075
13.7%
feb 881
 
11.2%
aug 493
 
6.3%
jan 472
 
6.0%
oct 312
 
4.0%
jun 302
 
3.9%
sep 281
 
3.6%
jul 225
 
2.9%
Other values (2) 272
 
3.5%

Length

2024-12-21T20:36:46.863915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may 2436
31.1%
nov 1093
13.9%
apr 1075
13.7%
feb 881
 
11.2%
aug 493
 
6.3%
jan 472
 
6.0%
oct 312
 
4.0%
jun 302
 
3.9%
sep 281
 
3.6%
jul 225
 
2.9%
Other values (2) 272
 
3.5%

Most occurring characters

ValueCountFrequency (%)
a 4636
19.7%
m 2596
11.0%
y 2436
10.4%
n 1867
7.9%
o 1405
 
6.0%
p 1356
 
5.8%
e 1274
 
5.4%
r 1235
 
5.2%
v 1093
 
4.6%
u 1020
 
4.3%
Other values (9) 4608
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23526
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4636
19.7%
m 2596
11.0%
y 2436
10.4%
n 1867
7.9%
o 1405
 
6.0%
p 1356
 
5.8%
e 1274
 
5.4%
r 1235
 
5.2%
v 1093
 
4.6%
u 1020
 
4.3%
Other values (9) 4608
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23526
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4636
19.7%
m 2596
11.0%
y 2436
10.4%
n 1867
7.9%
o 1405
 
6.0%
p 1356
 
5.8%
e 1274
 
5.4%
r 1235
 
5.2%
v 1093
 
4.6%
u 1020
 
4.3%
Other values (9) 4608
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23526
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4636
19.7%
m 2596
11.0%
y 2436
10.4%
n 1867
7.9%
o 1405
 
6.0%
p 1356
 
5.8%
e 1274
 
5.4%
r 1235
 
5.2%
v 1093
 
4.6%
u 1020
 
4.3%
Other values (9) 4608
19.6%

duration
Real number (ℝ)

Distinct973
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261.29061
Minimum5
Maximum2219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2024-12-21T20:36:47.419181image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile41
Q1113
median194
Q3324
95-th percentile711.95
Maximum2219
Range2214
Interquartile range (IQR)211

Descriptive statistics

Standard deviation236.20327
Coefficient of variation (CV)0.90398682
Kurtosis9.8382106
Mean261.29061
Median Absolute Deviation (MAD)96
Skewness2.5687501
Sum2049041
Variance55791.986
MonotonicityNot monotonic
2024-12-21T20:36:47.983952image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158 38
 
0.5%
113 36
 
0.5%
136 36
 
0.5%
99 35
 
0.4%
124 35
 
0.4%
110 34
 
0.4%
175 34
 
0.4%
104 33
 
0.4%
134 33
 
0.4%
122 33
 
0.4%
Other values (963) 7495
95.6%
ValueCountFrequency (%)
5 2
 
< 0.1%
6 6
 
0.1%
7 16
0.2%
8 14
0.2%
9 14
0.2%
10 11
0.1%
11 16
0.2%
12 11
0.1%
13 16
0.2%
14 11
0.1%
ValueCountFrequency (%)
2219 1
< 0.1%
2184 1
< 0.1%
2129 1
< 0.1%
2062 1
< 0.1%
2053 1
< 0.1%
1962 1
< 0.1%
1925 1
< 0.1%
1916 1
< 0.1%
1835 1
< 0.1%
1823 1
< 0.1%

campaign
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0642693
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2024-12-21T20:36:48.436506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile5
Maximum16
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5661093
Coefficient of variation (CV)0.7586749
Kurtosis8.164171
Mean2.0642693
Median Absolute Deviation (MAD)1
Skewness2.4257454
Sum16188
Variance2.4526984
MonotonicityNot monotonic
2024-12-21T20:36:48.838611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 3784
48.3%
2 2180
27.8%
3 870
 
11.1%
4 406
 
5.2%
5 247
 
3.1%
6 153
 
2.0%
7 93
 
1.2%
8 52
 
0.7%
9 24
 
0.3%
11 12
 
0.2%
Other values (6) 21
 
0.3%
ValueCountFrequency (%)
1 3784
48.3%
2 2180
27.8%
3 870
 
11.1%
4 406
 
5.2%
5 247
 
3.1%
6 153
 
2.0%
7 93
 
1.2%
8 52
 
0.7%
9 24
 
0.3%
10 10
 
0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 7
 
0.1%
11 12
 
0.2%
10 10
 
0.1%
9 24
 
0.3%
8 52
0.7%
7 93
1.2%

pdays
Real number (ℝ)

Distinct527
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.25287
Minimum1
Maximum871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2024-12-21T20:36:49.366948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile79
Q1133
median195
Q3326
95-th percentile370
Maximum871
Range870
Interquartile range (IQR)193

Descriptive statistics

Standard deviation111.83013
Coefficient of variation (CV)0.50091238
Kurtosis0.86378585
Mean223.25287
Median Absolute Deviation (MAD)93
Skewness0.51405676
Sum1750749
Variance12505.977
MonotonicityNot monotonic
2024-12-21T20:36:49.896835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 151
 
1.9%
92 138
 
1.8%
183 120
 
1.5%
91 115
 
1.5%
181 114
 
1.5%
370 94
 
1.2%
184 84
 
1.1%
364 72
 
0.9%
94 70
 
0.9%
95 70
 
0.9%
Other values (517) 6814
86.9%
ValueCountFrequency (%)
1 14
 
0.2%
2 36
0.5%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 11
 
0.1%
6 10
 
0.1%
7 6
 
0.1%
8 24
0.3%
9 12
 
0.2%
10 6
 
0.1%
ValueCountFrequency (%)
871 1
< 0.1%
854 1
< 0.1%
842 1
< 0.1%
831 1
< 0.1%
828 1
< 0.1%
826 1
< 0.1%
805 1
< 0.1%
804 1
< 0.1%
792 1
< 0.1%
784 1
< 0.1%

previous
Real number (ℝ)

Skewed 

Distinct39
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1842642
Minimum1
Maximum275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.4 KiB
2024-12-21T20:36:50.187387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile9
Maximum275
Range274
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.6141896
Coefficient of variation (CV)1.4490599
Kurtosis1546.6843
Mean3.1842642
Median Absolute Deviation (MAD)1
Skewness27.995396
Sum24971
Variance21.290746
MonotonicityNot monotonic
2024-12-21T20:36:50.499298image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 2637
33.6%
2 1980
25.2%
3 1089
13.9%
4 689
 
8.8%
5 437
 
5.6%
6 263
 
3.4%
7 190
 
2.4%
8 127
 
1.6%
9 88
 
1.1%
10 63
 
0.8%
Other values (29) 279
 
3.6%
ValueCountFrequency (%)
1 2637
33.6%
2 1980
25.2%
3 1089
13.9%
4 689
 
8.8%
5 437
 
5.6%
6 263
 
3.4%
7 190
 
2.4%
8 127
 
1.6%
9 88
 
1.1%
10 63
 
0.8%
ValueCountFrequency (%)
275 1
 
< 0.1%
58 1
 
< 0.1%
55 1
 
< 0.1%
51 1
 
< 0.1%
40 1
 
< 0.1%
38 2
< 0.1%
37 2
< 0.1%
35 1
 
< 0.1%
32 1
 
< 0.1%
30 3
< 0.1%

poutcome
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size486.8 KiB
failure
4679 
other
1750 
success
1413 

Length

Max length7
Median length7
Mean length6.5536853
Min length5

Characters and Unicode

Total characters51394
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfailure
2nd rowother
3rd rowfailure
4th rowother
5th rowfailure

Common Values

ValueCountFrequency (%)
failure 4679
59.7%
other 1750
 
22.3%
success 1413
 
18.0%

Length

2024-12-21T20:36:50.852580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T20:36:51.126972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
failure 4679
59.7%
other 1750
 
22.3%
success 1413
 
18.0%

Most occurring characters

ValueCountFrequency (%)
e 7842
15.3%
r 6429
12.5%
u 6092
11.9%
f 4679
9.1%
a 4679
9.1%
i 4679
9.1%
l 4679
9.1%
s 4239
8.2%
c 2826
 
5.5%
o 1750
 
3.4%
Other values (2) 3500
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7842
15.3%
r 6429
12.5%
u 6092
11.9%
f 4679
9.1%
a 4679
9.1%
i 4679
9.1%
l 4679
9.1%
s 4239
8.2%
c 2826
 
5.5%
o 1750
 
3.4%
Other values (2) 3500
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7842
15.3%
r 6429
12.5%
u 6092
11.9%
f 4679
9.1%
a 4679
9.1%
i 4679
9.1%
l 4679
9.1%
s 4239
8.2%
c 2826
 
5.5%
o 1750
 
3.4%
Other values (2) 3500
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7842
15.3%
r 6429
12.5%
u 6092
11.9%
f 4679
9.1%
a 4679
9.1%
i 4679
9.1%
l 4679
9.1%
s 4239
8.2%
c 2826
 
5.5%
o 1750
 
3.4%
Other values (2) 3500
6.8%

Interactions

2024-12-21T20:36:37.563115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:21.580503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:23.720979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:25.944008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:27.916655image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:29.915616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:32.373149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:35.450466image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:37.828534image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:21.851135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:23.974382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:26.217642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:28.184376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:30.172256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:32.764656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:35.822241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:38.099968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:22.126767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:24.405446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:26.477309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:28.441404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:30.439909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:33.131046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:36.098553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:38.355166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:22.402178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:24.659629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:26.697201image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:28.691546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:30.689753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:33.508466image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:36.339361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:38.604438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:22.658772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:24.905220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:26.942867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:28.929370image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:31.113857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:33.911837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:36.585986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:38.835887image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:22.909526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:25.174409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:27.164927image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:29.177561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:31.357051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:34.262121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:36.808341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:39.106366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:23.200886image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:25.479097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:27.435350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:29.436761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:31.716923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:34.650638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:37.071638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:39.369659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:23.452970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:25.709050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:27.673623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:29.680790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:32.037983image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:35.021847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-21T20:36:37.322717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-21T20:36:51.647137image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
agebalancecampaigncontactcustomer_idday_of_weekdurationeducationhousingjobloanmarital_statusmonthpdayspoutcomeprevious
age1.0000.0870.0140.2490.004-0.0000.0260.1810.3300.2990.1000.3510.088-0.0780.1360.031
balance0.0871.000-0.0200.0550.1070.0800.0770.0570.0590.0500.0610.0290.034-0.1870.0000.012
campaign0.014-0.0201.0000.112-0.016-0.017-0.0960.0350.0510.0200.0240.0000.0420.0830.0960.167
contact0.2490.0550.1121.0000.0630.0480.0400.1080.0770.1480.0330.0450.0460.0350.0390.004
customer_id0.0040.107-0.0160.0631.000-0.0850.1670.1340.5570.1400.1960.0830.588-0.0410.3160.106
day_of_week-0.0000.080-0.0170.048-0.0851.000-0.0150.0890.2360.0690.0840.0440.345-0.0930.113-0.011
duration0.0260.077-0.0960.0400.167-0.0151.0000.0220.1030.0250.0250.0000.051-0.0310.1090.008
education0.1810.0570.0350.1080.1340.0890.0221.0000.1320.5450.0530.1220.1200.1440.0720.020
housing0.3300.0590.0510.0770.5570.2360.1030.1321.0000.3660.1030.0480.4610.3770.3110.000
job0.2990.0500.0200.1480.1400.0690.0250.5450.3661.0000.1170.2260.1020.0960.1340.000
loan0.1000.0610.0240.0330.1960.0840.0250.0530.1030.1171.0000.0770.1470.0660.1150.022
marital_status0.3510.0290.0000.0450.0830.0440.0000.1220.0480.2260.0771.0000.0630.0450.0430.000
month0.0880.0340.0420.0460.5880.3450.0510.1200.4610.1020.1470.0631.0000.2740.2270.000
pdays-0.078-0.1870.0830.035-0.041-0.093-0.0310.1440.3770.0960.0660.0450.2741.0000.182-0.099
poutcome0.1360.0000.0960.0390.3160.1130.1090.0720.3110.1340.1150.0430.2270.1821.0000.019
previous0.0310.0120.1670.0040.106-0.0110.0080.0200.0000.0000.0220.0000.000-0.0990.0191.000

Missing values

2024-12-21T20:36:39.758211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-21T20:36:40.377429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idagejobmarital_statuseducationbalancehousingloancontactday_of_weekmonthdurationcampaignpdayspreviouspoutcome
0133admin.marriedtertiary882nonotelephone21oct3911513failure
1242admin.singlesecondary-247yesyestelephone21oct51911661other
2333servicesmarriedsecondary3444yesnotelephone21oct1441914failure
3436managementmarriedtertiary2415yesnotelephone22oct731864other
4536managementmarriedtertiary0yesnotelephone23oct14011433failure
5644blue-collarmarriedsecondary1324yesnotelephone25oct1191892other
6726techniciansingletertiary172noyestelephone4nov2111404other
7851admin.singlesecondary3132nonotelephone5nov44911761failure
8933unemployeddivorcedsecondary1005yesnotelephone10nov17511742failure
91030admin.marriedsecondary873yesnotelephone12nov11911673success
customer_idagejobmarital_statuseducationbalancehousingloancontactday_of_weekmonthdurationcampaignpdayspreviouspoutcome
7832783332blue-collarmarriedsecondary136nonocellular16nov20611883success
7833783475retireddivorcedtertiary3810yesnocellular16nov26211831failure
7834783528self-employedsingletertiary159nonocellular16nov4492334success
7835783659managementmarriedtertiary138yesyescellular16nov16221875failure
7836783768retiredmarriedsecondary1146nonocellular16nov21211876success
7837783834blue-collarsinglesecondary1475yesnocellular16nov1166353012other
7838783953managementmarriedtertiary583nonocellular17nov22611844success
7839784073retiredmarriedsecondary2850nonocellular17nov3001408failure
7840784172retiredmarriedsecondary5715nonocellular17nov112751843success
7841784237entrepreneurmarriedsecondary2971nonocellular17nov361218811other